I.J. Information Technology and Computer Science, 2016, 01, 33-39
Published Online January 2016 in MECS (http://www.mecs-press.org/)
DOI: 10.5815/ijitcs.2016.01.04
Extraction of Root Words using Morphological
Analyzer for Devanagari Script
Sharvari S. Govilkar
Department of Information Technology, TSEC, Mumbai, India.
E-mail: [email protected]
J. W. Bakal and Sagar R. Kulkarni
SJCOE, Mumbai, India
Department of Computer Engineering, PIIT, New Panvel, India
E-mail: [email protected], [email protected]
Abstract—In India, more than 300 million people use
Devanagari script for documentation. In Devanagari
script, Marathi and Hindi are mainly used as primary
language of Maharashtra state and national language of
India respectively. As compared with English script,
Devanagari script is reach of morphemes. Thus the
lemmatization of Devanagari script is quite complex than
that of English script. There is lack of resources for
Devanagari script such as WordNet, ontology
representation, parsing the keywords and their part of
speech. Thus the overall task of information retrieval
becomes complex and time consuming. Devanagari script
document always carries suffixes which may cause
problem in accurate information retrieval. We propose a
method of extracting root words from Devanagari script
document which can be used for information retrieval,
text summarization, text categorization, ontology building
etc. An attempt is made to design the Morphological
Analyzer for Devanagari script. We have designed
CORPUS containing more than 3000 possible stop words
and suffixes for Marathi language. Morphological
Analyzer can acts as a preliminary stage for developing
any information retrieval application in Devanagari script.
We have conducted the experiments on randomly
selected Marathi documents and we found the accuracy
of designed morphological analyzer is up to 96%.
Index Terms—Morphological analyzer, text mining,
tokenization, stop words in Devanagari, suffixes in
Devanagari, stemming, removing inflections using rules.
I. INTRODUCTION
When user needs to retrieve some information from the
Devanagari script document depending on the query,
he/she may get some irrelevant information or may lose
some important information because Devanagari script
contains too many suffixes and inflections. It is also
morphologically rich script.
Devanagari script includes many languages such as
Marathi, Hindi, Sanskrit, Prakrit etc. The Morphological
analyzer operations are performed on Marathi language
document. Figure1 shows that Marathi language has 13
Copyright © 2016 MECS
vowels and 36 consonants. Figure2 shows the modifiers
used in Devanagari script. Marathi is official language of
the state of Maharashtra (India). With 300 million fluent
speakers worldwide, Devanagari ranks as the second
most spoken language in India and fifteenth most in the
world [1].
Fig.1. Vowels and Constants in Devanagari Script
Fig.2. Modifiers used in Devanagari Script
When one wants to work on information extraction,
text summarization, text mining, retrieval based on
ontology, the common problem arises for all these
applications is to find exact information for the user
depending on the query given. If the query word is
present in the document then the information / sentences
are retrieved related to the query term. When the system
compares query word with the input Devanagari script
document then even though the term present in the
document, mismatch may occur because of suffixes and
inflections. These suffixes and inflections in Marathi
makes the task of information retrieval, text mining very
complicated. The solution to this is to extract root words
by removing suffixes and inflections of the word. This
process is called as Morphological Analysis. Performing
stemming and removing inflections of the word is very
important task to retrieve relevant information from the
collection of document. There are variety of languages
which uses Devanagari script such as Marathi, Hindi,
Sanskrit and Prakrit etc. It is easy to extract root words by
applying stemming algorithm for language like English.
As Devanagari script is morphologically rich, stemming,
Suffix removal and inflection striping is complex. For
I.J. Information Technology and Computer Science, 2016, 01, 33-39
34
Extraction of Root Words using Morphological Analyzer for Devanagari Script
keyword based information retrieval, root words plays
vital role in improving the performance of retrieval
system. As Devanagari script is morphologically rich
script which contains many suffixes and inflections in the
words, retrieving accurate information becomes complex
task. For effective information extraction there is a need
to extract root words from input document. This problem
demonstrates necessity of morphological analyzer.
We have performed the experiment on Marathi
language in which input is randomly selected Marathi
document and output is series of root words. We have
designed CORPUS containing more than 3000 possible
stop words and suffixes for Marathi language which are
frequently used in Devanagari script. The CORPUS plays
very important role for filtration of input document.
Performing stemming and removing inflections of the
word is very important task to retrieve relevant and
detailed information from the document.
Example: In information retrieval system if we want
to search information about “भारत”, then query will not
result all the sentences from document containing words
like “ भारताची, भारतासाठी, भारतामध्ये, भारताने” due to
suffixes “ची, साठी, मध्ये, ने”
and inflection of ‘आ’ in
‘ता’ in above words. This problem can be overcome by
using Morphological operation on the each term to get
actual root word. The root word for all the above words is
“भारत” Once this root word is found the information
retrieval becomes easy because the query term will match
with all the above words and hence results all the
sentences containing this term. Thus there is need to find
out the root words from the document so that retrieval
system will provide relevant information.
There has been a significant improvement in the
research related to Devanagari script document. In recent
year’s research towards Indian languages is getting
increasing attention. Our proposed architecture can be
used to design Morphological analyzer for any language.
II. LITERATURE REVIEW
The morphological analysis for Devanagari script
document requires many pre-processing stages such as
tokenization, keyword recognition, stop word removal,
stemming, removing inflections from the word etc. In
India there is very less work has been reported in
literature on Devanagari script. Extraction of root words
is the preliminary task for any natural language
processing activity. The Lemmatization and stop-word
elimination are well studied for English and a few
European Languages. Also there is no work done on the
validation of script using UTF-8 as in [8]. Even, lexical
analysis such as stemming for Marathi is not used in the
modern and popular search engines such as Yahoo and
Google. The stop word removal is very important task as
it doesn’t contribute much in information retrieval
process. This stop word removal system has already been
implemented for English language. As discussed by
Copyright © 2016 MECS
Manish Shrivastava, Pushpak Bhattacharya in [1] and
Ashish Almeida, Pushpak Bhattacharya in [2], the
inclusion of suffixes in indexing and stop-words
elimination effect on the retrieval performance. An
important observation is that the suffixes in Marathi
language can also contribute to the semantics of the
document and hence improves the retrieval performance
by removing all suffixes from the document. The removal
of inflectional suffixes are not possible by normal
stemming operation so there is need of stemmer which is
used to remove all the possible suffixes from the keyword
and gives word stem. According to Upendra Mishra,
Chandra Prakash as given in [3] the Maulik stemmer is
purely based on Devanagari script (Hindi) and it uses the
Hybrid approach (combination of brute force and suffix
removal approach). In [4] the author evaluates a rulebased and an unsupervised Marathi stemmer. The rulebased stemmer uses a set of manually extracted suffix
stripping rules whereas the unsupervised approach learns
suffixes automatically from a set of words extracted from
raw Marathi text. To detect suffixes automatically using
unsupervised approach, Marathi WordNet is required
which is not available for public use. Character
recognition is very important for validation of script. The
author in [7] uses UTF-8 provided by Unicode
organization as in [8] for character recognition for Hindi
script. The paper represents light stemmer which removes
all of these suffixes, the longest suffix first. The list of 27
common suffixes is used in this paper. For Devanagari
there are too many suffixes possible that may occur in the
document. The CORPUS for all possible Stop words and
Suffixes in Devanagari is not available for public use. We
have designed rules for inflectional suffix stripping
operation to achieve desired output as a root words.
Availability of Resources:
The stop words are most frequently occurring words in
Devanagari script document and carry no meaning for
information retrieval system. The suffixes are always
attached with the root words. The presence of suffixes
may degrade the retrieval performance. For effective
morphological analysis there is need of Stop words and
Suffix corpus. Unfortunately these resources are not
available for public use.
III. PROPOSED ARCHITECTURE
The objective of this paper is to extract root words
from Devanagari script document using Morphological
Analyzer. Very less work has been done in India on
Devanagari script due to unavailability of resources such
as WordNet, Ontology and Corpus etc. Unless we extract
root words from the input document, we cannot achieve
effective result because retrieval system requires exact
match of query word with words in the input document.
The presence of such words degrades the matching query
term efficiency.
We have developed Morphological Analyser for
Devanagari script which gives root word by removing
I.J. Information Technology and Computer Science, 2016, 01, 33-39
Extraction of Root Words using Morphological Analyzer for Devanagari Script
suffixes and inflections of the words. After extracting
root words the query term can be easily compared with
root words from the input document so as to generate
relevant result in information retrieval system. This
system can acts as preliminary stage for all information
retrieval system such as Text mining, Text summarization,
Categorization etc.
The input document is in Marathi language. First of all
the input document is filtered for the purpose of removing
all the special characters. The input document is then
scanned to validate whether the input contains any other
script or not. If so we will eliminate those irrelevant
characters to maintain pure Devanagari script document.
This can be done by using UTF-8. The pure Devanagari
script document is then tokenized so that we can deal
with each keyword separately. The customized corpus is
used for removing stop words and suffixes from the
document. This step is important as stop words are most
frequently occurring words in Devanagari script and carry
no meaning for keyword based information retrieval
system. Suffix removal lead to achieve stem of word
which will be further used for extracting actual Root
word. The rules are created to remove inflections of the
word so that user can get accurate root word.
with UTF-8 is time consuming which leads to memory
wastage. The special characters such as “ ” ‘ ’, . / ? [ ]
{ }: ; \ | ~ ! @ # $ % ^ & * ( ) _ - = + < > are frequently
used in many language scripts. These characters will not
contribute towards final result.
Step 2: Script validation
Keyword recognition is very important stage in text
mining because the resultant information is totally
depends on the language and nature of query supplied to
the system. The input document may contain some words
or sentences in other script or language. Here we are
analyzing whether the input document is in Devanagari
script or not. The words which are not valid to
Devanagari script are simply removed from further
processing. To perform this operation we have used
Unicode values called UTF-8[8] for Devanagari script
document. We compared UTF-8 list with each character
of each token, if match found the character is valid and
allowed otherwise removed from the document. The aim
of this phase is to maintain pure Devanagari script
document as input to Morphological Analyzer.
Algorithm for Validation of Input document:
1.
Devanagari Script
Document
2.
3.
4.
5.
Filtration of
Document
Stemming
(Removing suffixes)
Script Validation
35
6.
7.
8.
Apply filtration algorithm. If already applied then
ignore this step.
Use the character set as UTF-8
Scan the input document.
Compare each character from scanned input
document with UTF-8.
If character is present in the UTF-8, then it is valid
to Devanagari script otherwise not.
Ignore all the invalid Devanagari script characters.
Repeat step 3 till all characters from input script
document get verified.
Store all the valid Devenagari character, words in
file to process further.
Step 3: Tokenization
Tokenization
Morphological
Analysis
Stop word
removal
Step 4: Stop word removal
Fig.3. Morphological Analyzer for Devanagari Script
The input to the system contains randomly selected
Devanagari script documents especially in Marathi
language. The following steps are used for extracting root
words from Devanagari script documents.
Step 1: Filtration of Devanagari Script
As presence of special characters in Devnagari
documents degrades the performance, it needs to be
removed. This removal of special characters from
Devanagari script is called as filtration of document.
Token creation of special characters and its recognition
Copyright © 2016 MECS
The pure Devanagari script document is passed
through tokenization to get valid tokens which can be
also called as Lexicons. With the help of lexical analyzer
one can tokenize the input document as one token per line.
Here space is used to generate tokens.
Stop words are the most frequently occurring words
within the collection of document and thus they have very
little discriminatory value. Stop words represent noise,
and may take more time on processing and reduce overall
retrieval performance. They tend to create huge posting
lists which take up lots of disk space and degrade the
performance of retrieval .Thus, it is usual practice to
identify and eliminate stop-words in the process of
searching. While searching for particular keyword using
query, the system may include all the records/tokens of
the input document for the process of searching
regardless of their relevance.
The stop word makes up a large portion of the text in
the document. Thus it is required to remove such stop
I.J. Information Technology and Computer Science, 2016, 01, 33-39
36
Extraction of Root Words using Morphological Analyzer for Devanagari Script
words from index to save the searching time, and also to
enhance searching performance. We have designed
Corpus of all the possible stop words that may occur
frequently in the Devanagari script especially in Marathi
language. Table1 shows few examples of stop words that
are normally used in Marathi language.
deal with inflections of the word. Table2 shows few
examples of suffixes that are normally used in Marathi
language.
Table 2. Examples of Suffixes in Devanagari Script (Marathi)
Table 1. Examples of Stop words used in Devanagari Script (Marathi)
च्या
न्या
र्या
ऱ्या
ख्या
वर
साठी
पासून
तून
कडू न
कडू न
मुळे
असं
अथवा
अशा
असा
साठी
प्रमाणे
वरून
वर
आणखी
आणण
आत्ता
अरे च्चा
च
चे
तील
पयंत
इथे
इथं
चा
कडेही
नी
नीही
मधले
तही
तपण
तसुद्धा
कडे
पाशी
पयंत
पूववक
तच
हून
प्रमाणे
लेल्या
अरे पण
इतर
इतके
इतक्यात
इत्यादी
एवढ
ककवा
कारण
की
कें व्हा
जे
जेथे
ज्याच्या
जेंव्हा
जी
णजथले
णजथे
णजच्यामुळे
तरी
तर
तरच
त्याच्या
त्यांना
तेंव्हा
नेमकी
परं तु
त्याचा
बरं
बापरे
म्हणजे
पण
मध्ये
सध्या
व
मधे
वारं वार
ह्यावर
ह्यांच्यावर
वरचेवर
याच्यासाठी
याच्यात
याच्यातच
यात
यांचे
यांसाठी
यांहून
यावर
या
त्यामुळे
त्यामुळेच
Step 5: Stemming
Suffix stripping is an important step required in a
number of natural language processing applications such
as information retrieval, text summarization, document
clustering etc. The widely used method for this
processing is Stemmer which uses a suffix list to remove
suffixes from words. The stem is not necessarily the
linguistic root of the word. We have designed Corpus of
all possible suffixes that occur frequently in the
Devanagari script. The corpus is used to remove suffixes
from input document.
The result of stemming is stem of word that can be
given as input to Morphological Analyzer for further
processing. The observation is made on the result of
Stemming and it is found that stem of word normally
contains inflections. The inflections in the stem word
cannot be removed using simple stemming operation. To
do this we must have some standards which will easily
Copyright © 2016 MECS
Step 6: Morphological Analyzer
The aim of morphological analysis is to recognize the
inner structure of the word. A morphological analyzer is
expected to produce root words for a given input
document. Devanagari script is morphologically rich
language in which the case markers and postposition
markers are usually manifested as suffixes. The root and
stem of word may differ in their forms. The words after
stemming are analyzed to check whether they are
inflected or not. This can be done by creating and
comparing rules with the words. If stem word is inflected
then the root word is formed by addition of replacement
characters with stem word.
The rules are formed to find actual root word. The
system searches the perfect match from the set of rules
and if the keyword after stemming called stem of word
contains any inflection then those inflections are removed
from the keyword. There is a need to design some
standard set of rules which will enable the system to
process the stem of words and find the actual root word.
Rule Format:
List of Characters  Replacement Character. e.g. मा
मी मु मे  म. The meaning of this rule is whenever the
word ends with “ मा मी मु मे” or has inflection “ आ ई उ
ए”
are replaced by the character “ म”
with inflection
“ अ” .
We can use following data to get the root for inflected
word:
1.
2.
3.
List of all the possible suffixes.
Rules for inflected words to be replaced by
another character.
The replacements characters to be made after
removal of suffix so that valid root can be formed.
I.J. Information Technology and Computer Science, 2016, 01, 33-39
Extraction of Root Words using Morphological Analyzer for Devanagari Script
IV. EXPERIMENTS AND RESULTS
Input to System:
The input for the system contains Devanagari script
document in Marathi language.
“सागर मराठीमध्ये शबदांचा मूळ शबद शोधण्याचे काम
करत
आहे. मराठीतील मूळ शबदाचा उपयोग करून कोणतीही
मराठी माणहती सुलभपणे णमळवता येईल. आजही मराठीमधला
37
possible suffixes from the script. Suffixes are those
characters that normally appear at the end of words. The
suffixes are of two types, plain suffixes and complex
suffixes. The system identifies all those suffixes by
comparing it with the Corpus of suffixes we designed.
The Corpus contains all the possible suffixes used in
Devanagari script document for Marathi language. Table4
shows possible suffixes found in the given input.
Table 4. Suffixes Found in the Input Devanagari Script.
Suffixes Found
Example of Occurrence
च
खूपच
ही, ले
कोणतीही, झालेले
मध्ये, मधला
भारतामध्ये, मराठीमधला
वर
लीपीवर
चा, चे
शबदांचा, शोधण्याचे
ता, तील
णमळवता, मराठीतील
पणे
सुलभपणे
साठी
संशोधनासाठी
शबदकोश संशोधनासाठी उपलबध होणे कठीण आहे. त्यामुळेच,
भारतामध्ये देवनागरी लीपीवर खूपच थोड्या प्रमाणात संशोधन
झालेले आहे. (**End of Input Text**).”
Filtration:
When this input is given to our system the filtration
will remove all the special characters from the system as
they are not part of further processing.
Script validation:
In this step, the characters which are not valid to
Devanagari script are simply removed from the input.
This task is done using UTF-8. Here the output we obtain
is pure Devanagari script. The English characters in given
example is removed from the input to maintain pure
Devanagari script document.
Tokenization:
The output generated after keyword recognition is fed
to tokenization process where the input Devanagari script
is tokenized to ease the further processing. The
tokenization is done by detecting spaces between the
keyword i.e. when space is reached the token get formed.
All the tokens are then given to stop word removal
process.
The output after stemming contains Stem of words
which may have many inflections.
Morphological Analyzer:
The inflections present in the stem of words can be
removed by using Morphological analyzer. The analyzer
uses the rule based approach to remove inflections from
the stem word. Table 5 shows some examples of Rules
used for given input script.
Stop word Removal:
Table 5. Examples of Rules used in Morphological Analyzer.
The system finds all the possible stop words from the
input by comparing it with Corpus of Stop words we have
designed. The matched stop words are ignored from
further process as they don’t carry any meaningful
information. Table 3 shows few stop words and their
occurrences found in the given input script.
List of Characters
Replacement Character
ता णत ती तु तू ते तै तो तौ
त
ना णन नी नु नू ने नै नो नौ
न
णा णण णी णु णे णो ण्या
ण
Table 3. Stop words Found in the Input Devanagari Script.
Stop words Found
Example of Occurrence
त्यामुळेच
त्यामुळेच भारत…….
ला ली लां ले
ल
कोणतीही
कोणतीही माणहती……
षे षां
षा
Stemming:
This step plays an important role as it removes all the
Copyright © 2016 MECS
The output of the morphological analyzer is divided
into three columns such as Original word, Stem of word
and Root word as shown in Table 6. The stop words
I.J. Information Technology and Computer Science, 2016, 01, 33-39
38
Extraction of Root Words using Morphological Analyzer for Devanagari Script
found in the input script are indicated as S_W.
Table 6. Result of Morphological Analyzer
Original Word
Stem Word
Root Word
सागर
सागर
सागर
मराठीमध्ये
मराठी
मराठी
शबदांचा
शबदां
शबद
मूळ
मूळ
मूळ
शबद
शबद
शबद
शोधण्याचे
शोधण्या
शोधण
काम
काम
काम
कर
कर
आहे
आहे
आहे
मराठीतील
मराठी
मराठी
मूळ
मूळ
मूळ
शबदाचा
शबदा
शबद
उपयोग
उपयोग
उपयोग
करून
करून
करून
कोणतीही
S_W
S_W
मराठी
मराठी
मराठी
माणहती
माणहती
माणहत
सुलभपणे
सुलभ
सुलभ
णमळवता
णमळवता
णमळवत
येईल
येईल
येईल
आजही
आज
आज
मराठीमधला
मराठी
मराठी
शबदकोश
शबदकोश
शबदकोश
संशोधनासाठी
संशोधना
संशोधन
उपलबध
उपलबध
उपलबध
होणे
होणे
होणे
कठीण
कठीण
कठीण
आहे
आहे
आहे
त्यामुळेच
S_W
S_W
भारतामध्ये
भारता
भारत
देवनागरी
देवनागरी
देवनागरी
लीपीवर
लीपी
लीपी
खूपच
खूप
खूप
थोड्या
थोड्या
थोड
प्रमाणात
प्रमाणा
प्रमाण
संशोधन
संशोधन
संशोधन
झालेले
झालेले
झालेल
आहे
आहे
आहे
करत
Copyright © 2016 MECS
V. CONCLUSION
Information Retrieval from Devanagari script
document needs extract root words to do further
processing. An attempt is made to design the
Morphological Analyzer for Devanagari script. There are
many factors that may affect the performance of the
system for Devanagari script. Stemming alone cannot
find the relevant information if the words in the document
have more inflections. Inflections in word may degrade
overall performance of search. Accuracy of
Morphological analyzer is totally depends on how
effectively one can generate rules for eliminating
inflections from the word.
The proposed morphological analyzer acts as a
preliminary step to achieve relevant output for the
applications like text mining, text summarization,
semantic Information retrieval based on ontology etc. by
removing suffixes and inflections of the string. Our
proposed approach will minimize inflections of words so
that the further task will become easy for retrieving
desired information. The research towards regional
languages is increasing day by day. There is a large scope
to design the complete resources for Devanagari script
such as WordNet, Ontology and Corpus etc. to achieve
better result in information retrieval applications.
REFERENCES
[1]
Pushpak Bhattacharya, Manish Shrivastava, Nitin
Agrawal, Bibhuti Mohapatra, Smriti Singh, IIT Bombay
“Morphology Based Natural Language Processing tools
for Indian Languages” 2012.
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I.J. Information Technology and Computer Science, 2016, 01, 33-39
Extraction of Root Words using Morphological Analyzer for Devanagari Script
39
Authors’ Profiles
Sharvari Govilkar is Associate
professor in Computer Engineering
Department, at PIIT, New Panvel,
University of Mumbai, India. She has
received her M.E in Computer
Engineering from University of Mumbai.
Currently She is pursuing her Ph.D. in
Information Technology from University
of Mumbai. She is having eighteen years of experience in
teaching. Her areas of interest are Text Mining, Natural
language processing, Information Retrieval, domain specific
ontology construction etc.
Dr. J. W. Bakal received M.Tech in
Electronics
Engineering,
from
Marathwada University. Later, He has
completed his Ph.D. in the field of
Computer Engineering from Bharati
University, Pune. He is a PhD supervisor
in CSE at University of Mumbai. He is
presently working as principal at the S.S.
Jondhale College of Engineering, Thane,
India. He was a chairman of board of studies in Information
Technology in University of Mumbai. His research interests are
Telecomm Networking, Mobile Computing and Information
Security. He has publications in journals, conference
proceedings, and books in his credits. During his academics
tenure, he has attended, organized and conducted training
programs in Computer and Electronics branches. He is life
member of professional societies such as IETE, ISTE INDIA.
He is also a member of IEEE. He has prominently worked for
IETE as a chairman, Mumbai section.
Sagar Kulkarni is Assistant Professor in
Computer Engineering department at
PIIT, New Panvel, University of Mumbai,
India. He has completed M.E. in
Computer Engineering from University
of Mumbai, India. Sagar has received BE
in CSE from Shivaji University,
Kolhapur. He is having Eight years of
experience in teaching. His area of
interest are Text Mining and Summarization, System
Programming and Compiler construction, Natural Language
Processing, Information Retrieval etc.
How to cite this paper: Sharvari S. Govilkar, J. W. Bakal,
Sagar R. Kulkarni,"Extraction of Root Words using
Morphological Analyzer for Devanagari Script", International
Journal of Information Technology and Computer
Science(IJITCS), Vol.8, No.1, pp.33-39, 2016. DOI:
10.5815/ijitcs.2016.01.04
Copyright © 2016 MECS
I.J. Information Technology and Computer Science, 2016, 01, 33-39